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yhong55/
HORM

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This repository provides trained checkpoints of reactive Machine Learning Interatomic Potentials (MLIPs) developed using the Hessian dataset for Optimizing Reactive MLIP (HORM) — the largest Hessian-labeled quantum chemistry dataset to date. These models are specifically optimized for transition state (TS) characterization and are trained using a Hessian-informed strategy that significantly improves Hessian prediction accuracy and TS search robustness.

Filename	Model	Training Method
alpha_orig.ckpt	AlphaNet	Energy-Force Training
alpha.ckpt	AlphaNet	Energy-Force-Hessian Training
left_orig.ckpt	LEFTNet	Energy-Force Training
left.ckpt	LEFTNet	Energy-Force-Hessian Training
left-df_orig.ckpt	LEFTNet-df	Energy-Force Training
left-df.ckpt	LEFTNet-df	Energy-Force-Hessian Training
eqv2_orig.ckpt	EquiformerV2	Energy-Force Training
eqv2.ckpt	EquiformerV2	Energy-Force-Hessian Training

yhong55/HORM

yhong55/
HORM